import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


A						=			logging.get_logger(__name__)  # pylint: disable=invalid-name





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Tuple     ,  snake_case__     :     WhisperForConditionalGeneration     ,  snake_case__     :     WhisperProcessor     ,  snake_case__     :     AutoencoderKL     ,  snake_case__     :     CLIPTextModel     ,  snake_case__     :     CLIPTokenizer     ,  snake_case__     :     UNetaDConditionModel     ,  snake_case__     :     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,  snake_case__     :     StableDiffusionSafetyChecker     ,  snake_case__     :     CLIPImageProcessor     ,  )      ->	Dict:
				super().__init__()

				if safety_checker is None:
						logger.warning(
						    f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
						    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
						    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
						    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
						    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
						    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'		)

				self.register_modules(
				    speech_model=snake_case__     ,  speech_processor=snake_case__     ,  vae=snake_case__     ,  text_encoder=snake_case__     ,  tokenizer=snake_case__     ,  unet=snake_case__     ,  scheduler=snake_case__     ,  feature_extractor=snake_case__     ,  )
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     Optional[Union[str, int]] = "auto"		)      ->	Optional[int]:
				if slice_size == "auto":
						_lowerCamelCase				   =  self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(snake_case__		)
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Dict:
				self.enable_attention_slicing(snake_case__		)



		@torch.no_grad()
		def __call__(       self     :     Union[str, Any]     ,  snake_case__     :     List[Any]     ,  snake_case__     :     str=1_6_0_0_0     ,  snake_case__     :     int = 5_1_2     ,  snake_case__     :     int = 5_1_2     ,  snake_case__     :     int = 5_0     ,  snake_case__     :     float = 7.5     ,  snake_case__     :     Optional[Union[str, List[str]]] = None     ,  snake_case__     :     Optional[int] = 1     ,  snake_case__     :     float = 0.0     ,  snake_case__     :     Optional[torch.Generator] = None     ,  snake_case__     :     Optional[torch.FloatTensor] = None     ,  snake_case__     :     Optional[str] = "pil"     ,  snake_case__     :     bool = True     ,  snake_case__     :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,  snake_case__     :     int = 1     ,  **snake_case__     :     List[Any]     ,  )      ->	Tuple:
				_lowerCamelCase				   =  self.speech_processor.feature_extractor(
				    snake_case__     ,  return_tensors='pt'     ,  sampling_rate=snake_case__		).input_features.to(self.device		)
				_lowerCamelCase				   =  self.speech_model.generate(snake_case__     ,  max_length=4_8_0_0_0_0		)

				_lowerCamelCase				   =  self.speech_processor.tokenizer.batch_decode(snake_case__     ,  skip_special_tokens=snake_case__     ,  normalize=snake_case__		)[
				    0
				]

				if isinstance(snake_case__     ,  snake_case__		):
						_lowerCamelCase				   =  1
				elif isinstance(snake_case__     ,  snake_case__		):
						_lowerCamelCase				   =  len(snake_case__		)
				else:
						raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(snake_case__		)}"""		)

				if height % 8 != 0 or width % 8 != 0:
						raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""		)

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(snake_case__     ,  snake_case__		) or callback_steps <= 0)
				):
						raise ValueError(
						    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
						    f""" {type(snake_case__		)}."""		)

				# get prompt text embeddings
				_lowerCamelCase				   =  self.tokenizer(
				    snake_case__     ,  padding='max_length'     ,  max_length=self.tokenizer.model_max_length     ,  return_tensors='pt'     ,  )
				_lowerCamelCase				   =  text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						_lowerCamelCase				   =  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
						logger.warning(
						    'The following part of your input was truncated because CLIP can only handle sequences up to'
						    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""		)
						_lowerCamelCase				   =  text_input_ids[:, : self.tokenizer.model_max_length]
				_lowerCamelCase				   =  self.text_encoder(text_input_ids.to(self.device		)		)[0]

				# duplicate text embeddings for each generation per prompt, using mps friendly method
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  text_embeddings.shape
				_lowerCamelCase				   =  text_embeddings.repeat(1     ,  snake_case__     ,  1		)
				_lowerCamelCase				   =  text_embeddings.view(bs_embed * num_images_per_prompt     ,  snake_case__     ,  -1		)

				# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
				# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
				# corresponds to doing no classifier free guidance.
				_lowerCamelCase				   =  guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
						_lowerCamelCase				   =  42
						if negative_prompt is None:
								_lowerCamelCase				   =  [''] * batch_size
						elif type(snake_case__		) is not type(snake_case__		):
								raise TypeError(
								    f"""`negative_prompt` should be the same type to `prompt`, but got {type(snake_case__		)} !="""
								    f""" {type(snake_case__		)}."""		)
						elif isinstance(snake_case__     ,  snake_case__		):
								_lowerCamelCase				   =  [negative_prompt]
						elif batch_size != len(snake_case__		):
								raise ValueError(
								    f"""`negative_prompt`: {negative_prompt} has batch size {len(snake_case__		)}, but `prompt`:"""
								    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
								    ' the batch size of `prompt`.'		)
						else:
								_lowerCamelCase				   =  negative_prompt

						_lowerCamelCase				   =  text_input_ids.shape[-1]
						_lowerCamelCase				   =  self.tokenizer(
						    snake_case__     ,  padding='max_length'     ,  max_length=snake_case__     ,  truncation=snake_case__     ,  return_tensors='pt'     ,  )
						_lowerCamelCase				   =  self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

						# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
						_lowerCamelCase				   =  uncond_embeddings.shape[1]
						_lowerCamelCase				   =  uncond_embeddings.repeat(1     ,  snake_case__     ,  1		)
						_lowerCamelCase				   =  uncond_embeddings.view(batch_size * num_images_per_prompt     ,  snake_case__     ,  -1		)

						# For classifier free guidance, we need to do two forward passes.
						# Here we concatenate the unconditional and text embeddings into a single batch
						# to avoid doing two forward passes
						_lowerCamelCase				   =  torch.cat([uncond_embeddings, text_embeddings]		)

				# get the initial random noise unless the user supplied it

				# Unlike in other pipelines, latents need to be generated in the target device
				# for 1-to-1 results reproducibility with the CompVis implementation.
				# However this currently doesn't work in `mps`.
				_lowerCamelCase				   =  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
				_lowerCamelCase				   =  text_embeddings.dtype
				if latents is None:
						if self.device.type == "mps":
								# randn does not exist on mps
								_lowerCamelCase				   =  torch.randn(snake_case__     ,  generator=snake_case__     ,  device='cpu'     ,  dtype=snake_case__		).to(
								    self.device		)
						else:
								_lowerCamelCase				   =  torch.randn(snake_case__     ,  generator=snake_case__     ,  device=self.device     ,  dtype=snake_case__		)
				else:
						if latents.shape != latents_shape:
								raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""		)
						_lowerCamelCase				   =  latents.to(self.device		)

				# set timesteps
				self.scheduler.set_timesteps(snake_case__		)

				# Some schedulers like PNDM have timesteps as arrays
				# It's more optimized to move all timesteps to correct device beforehand
				_lowerCamelCase				   =  self.scheduler.timesteps.to(self.device		)

				# scale the initial noise by the standard deviation required by the scheduler
				_lowerCamelCase				   =  latents * self.scheduler.init_noise_sigma

				# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
				# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
				# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
				# and should be between [0, 1]
				_lowerCamelCase				   =  'eta' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
				_lowerCamelCase				   =  {}
				if accepts_eta:
						_lowerCamelCase				   =  eta

				for i, t in enumerate(self.progress_bar(snake_case__		)		):
						# expand the latents if we are doing classifier free guidance
						_lowerCamelCase				   =  torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
						_lowerCamelCase				   =  self.scheduler.scale_model_input(snake_case__     ,  snake_case__		)

						# predict the noise residual
						_lowerCamelCase				   =  self.unet(snake_case__     ,  snake_case__     ,  encoder_hidden_states=snake_case__		).sample

						# perform guidance
						if do_classifier_free_guidance:
								_lowerCamelCase   ,							_lowerCamelCase				   =  noise_pred.chunk(2		)
								_lowerCamelCase				   =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

						# compute the previous noisy sample x_t -> x_t-1
						_lowerCamelCase				   =  self.scheduler.step(snake_case__     ,  snake_case__     ,  snake_case__     ,  **snake_case__		).prev_sample

						# call the callback, if provided
						if callback is not None and i % callback_steps == 0:
								callback(snake_case__     ,  snake_case__     ,  snake_case__		)

				_lowerCamelCase				   =  1 / 0.1_8215 * latents
				_lowerCamelCase				   =  self.vae.decode(snake_case__		).sample

				_lowerCamelCase				   =  (image / 2 + 0.5).clamp(0     ,  1		)

				# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
				_lowerCamelCase				   =  image.cpu().permute(0     ,  2     ,  3     ,  1		).float().numpy()

				if output_type == "pil":
						_lowerCamelCase				   =  self.numpy_to_pil(snake_case__		)

				if not return_dict:
						return image

				return StableDiffusionPipelineOutput(images=snake_case__     ,  nsfw_content_detected=snake_case__		)
import functools
from typing import Any


def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				list[str]							)   ->    bool:

		# Validation
		if not isinstance(UpperCamelCase			,       UpperCamelCase							) or len(UpperCamelCase							) == 0:
				raise ValueError('the string should be not empty string'							)

		if not isinstance(UpperCamelCase			,       UpperCamelCase							) or not all(
		    isinstance(UpperCamelCase			,       UpperCamelCase							) and len(UpperCamelCase							) > 0 for item in words							):
				raise ValueError('the words should be a list of non-empty strings'							)

		# Build trie
		_lowerCamelCase				   =  {}
		_lowerCamelCase				   =  'WORD_KEEPER'

		for word in words:
				_lowerCamelCase				   =  trie
				for c in word:
						if c not in trie_node:
								_lowerCamelCase				   =  {}

						_lowerCamelCase				   =  trie_node[c]

				_lowerCamelCase				   =  True

		_lowerCamelCase				   =  len(UpperCamelCase							)

		# Dynamic programming method
		@functools.cache
		def is_breakable(UpperCamelCase     :				int							) -> bool:
				if index == len_string:
						return True

				_lowerCamelCase				   =  trie
				for i in range(UpperCamelCase			,       UpperCamelCase							):
						_lowerCamelCase				   =  trie_node.get(string[i]			,       UpperCamelCase							)

						if trie_node is None:
								return False

						if trie_node.get(UpperCamelCase			,       UpperCamelCase							) and is_breakable(i + 1							):
								return True

				return False

		return is_breakable(0							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()